Tootfinder

Constraints on the Thompson optical depth to the CMB from the Lyman-$\alpha$ forest
Olga Garcia-Gallego, Vid Ir\v{s}i\v{c}, Martin G. Haehnelt, James S. Bolton
https://arxiv.org/abs/2510.00107

Constraints on the Thompson optical depth to the CMB from the Lyman-$α$ forest
We present the first constraints on the electron optical depth to reionization, $τ_{\mathrm{e}}$, from the Lyman-$α$ forest alone for physically motivated reionization models that match the reionization's end-point, $z_{\rm{end}}$, required by the same astrophysical probe, and for symmetric reionization models with fixed duration, $Δz$, commonly adopted in CMB reionization analyses. Compared to traditional estimates from the latter, the Lyman-$α$ forest traces the ionization state of the IG…

Formation Control via Rotation Symmetry Constraints
Zamir Martinez, Daniel Zelazo
https://arxiv.org/abs/2510.00676 https://arxiv.org/pdf/2510.00676

Formation Control via Rotation Symmetry Constraints
We present a distributed formation control strategy for multi-agent systems based only on rotation symmetry constraints. We propose a potential function that enforces inter-agent \textbf{rotational} symmetries, with its gradient defining the control law driving the agents toward a desired symmetric and planar configuration. We show that only $(n-1)$ edges, the minimal connectivity requirement, are sufficient to implement the control strategy, where $n$ is the number of agents. We further augmen…

Sutskever: We need new inductive constraints.
Marcus: That means neurosymbolic architectures! He said it. I was right all along. Why didn't anybody listen to me, the modern Cassandra? Woe is me.

You may know the project management triangle.
https://en.wikipedia.org/wiki/Project_management_triangle
It puts the quality of a product in the center and pictures the scope, budget and time as constraints.
There is this other triagle I see for companies:

CINDES: Classification induced neural density estimator and simulator
Dehao Dai, Jianqing Fan, Yihong Gu, Debarghya Mukherjee
https://arxiv.org/abs/2510.00367 https://

CINDES: Classification induced neural density estimator and simulator
Neural network-based methods for (un)conditional density estimation have recently gained substantial attention, as various neural density estimators have outperformed classical approaches in real-data experiments. Despite these empirical successes, implementation can be challenging due to the need to ensure non-negativity and unit-mass constraints, and theoretical understanding remains limited. In particular, it is unclear whether such estimators can adaptively achieve faster convergence rates …

Progressively Sampled Equality-Constrained Optimization
Frank E. Curtis, Lingjun Guo, Daniel P. Robinson
https://arxiv.org/abs/2510.00417 https://arxiv.org…

Progressively Sampled Equality-Constrained Optimization
An algorithm is proposed, analyzed, and tested for solving continuous nonlinear-equality-constrained optimization problems where the constraints are defined by an expectation or an average over a large (finite) number of terms. The main idea of the algorithm is to solve a sequence of equality-constrained problems, each involving a finite sample of constraint-function terms, over which the sample set grows progressively. Under assumptions about the constraint functions and their first- and secon…

Complexity of the Zero Set of a Matrix Schubert Ideal
Laura Escobar, Cesar Meza
https://arxiv.org/abs/2510.00131 https://arxiv.org/pdf/2510.00131

Complexity of the Zero Set of a Matrix Schubert Ideal
$T$-varieties are normal varieties equipped with an action of an algebraic torus $T$. When the action is effective, the complexity of a $T$-variety $X$ is $\dim(X)-\dim(T)$. Matrix Schubert varieties, introduced by Fulton in 1992, are $T$-varieties consisting of $n \times n$ matrices satisfying certain constraints on the ranks of their submatrices. In this paper, we focus on the complexity of certain torus-fixed affine subvarieties of matrix Schubert varieties. Concretely, given a matrix Schube…

Lattica: A Decentralized Cross-NAT Communication Framework for Scalable AI Inference and Training
Ween Yang, Jason Liu, Suli Wang, Xinyuan Song, Lynn Ai, Eric Yang, Tianyu Shi
https://arxiv.org/abs/2510.00183

Lattica: A Decentralized Cross-NAT Communication Framework for Scalable AI Inference and Training
The rapid expansion of distributed Artificial Intelligence (AI) workloads beyond centralized data centers creates a demand for new communication substrates. These substrates must operate reliably in heterogeneous and permissionless environments, where Network Address Translators (NATs) and firewalls impose significant constraints. Existing solutions, however, are either designed for controlled data center deployments or implemented as monolithic systems that tightly couple machine learning logi…

Low Resource Audio Codec Challenge Baseline Systems
Yusuf Ziya Isik, Rafa{\l} {\L}aganowski
https://arxiv.org/abs/2510.00264 https://arxiv.org/pdf/2510.002…

Low Resource Audio Codec Challenge Baseline Systems
The Low-Resource Audio Codec (LRAC) Challenge aims to advance neural audio coding for deployment in resource-constrained environments. The first edition focuses on low-resource neural speech codecs that must operate reliably under everyday noise and reverberation, while satisfying strict constraints on computational complexity, latency, and bitrate. Track 1 targets transparency codecs, which aim to preserve the perceptual transparency of input speech under mild noise and reverberation. Track 2 …

Redshifted civilizations, galactic empires, and the Fermi paradox
Chris Reiss, Justin C. Feng
https://arxiv.org/abs/2510.00377 https://arxiv.org/pdf/2510.0…

Redshifted civilizations, galactic empires, and the Fermi paradox
Given the vast distances between stars in the Milky way and the long timescales required for interstellar travel, we consider how a civilization might overcome the constraints arising from finite lifespans and the speed of light without invoking exotic or novel physics. We consider several scenarios in which a civilization can migrate to a time-dilated frame within the scope of classical general relativity and without incurring a biologically intolerable level of acceleration. Remarkably, the p…

A Computationally Efficient Finite Element Method for Shape Reconstruction of Inverse Conductivity Problems
Lefu Cai, Zhixin Liu, Minghui Song, Xianchao Wang
https://arxiv.org/abs/2510.00597

A Computationally Efficient Finite Element Method for Shape Reconstruction of Inverse Conductivity Problems
The inverse conductivity problem aims at determining the unknown conductivity inside a bounded domain from boundary measurements. In practical applications, algorithms based on minimizing a regularized residual functional subject to PDE constraints have been widely used to deal with this problem. However, such approaches typically require repeated iterations and solving the forward problem at each iteration, which leads to a heavy computational cost. To address this issue, we first reformulate …

Wireless Laser Power Transfer for Low-altitude Uncrewed Aerial Vehicle-assisted Internet of Things: Paradigms, Challenges, and Solutions
Chengzhen Li, Likun Zhang, Chuang Zhang, Jiahui Li, Changyuan Zhao, Ruichen Zhang, Geng Sun
https://arxiv.org/abs/2510.00477

Wireless Laser Power Transfer for Low-altitude Uncrewed Aerial Vehicle-assisted Internet of Things: Paradigms, Challenges, and Solutions
Low-altitude uncrewed aerial vehicles (UAVs) have become integral enablers for the Internet of Things (IoT) by offering enhanced coverage, improved connectivity and access to remote areas. A critical challenge limiting their operational capacity lies in the energy constraints of both aerial platforms and ground-based sensors. This paper explores WLPT as a transformative solution for sustainable energy provisioning in UAV-assisted IoT networks. We first systematically investigate the fundamental…

Geometry of the Milky Way's dark matter from dynamical models of the tilted stellar halo
Adam M. Dillamore, Jason L. Sanders
https://arxiv.org/abs/2510.00095 https://…

Geometry of the Milky Way's dark matter from dynamical models of the tilted stellar halo
The shape and orientation of the Milky Way's dark matter halo remain poorly constrained. Observations of the accreted stellar halo show that it is triaxial and tilted with respect to the disc. If this configuration is long-lived, it can be used to place constraints on the shape and orientation of the dark matter halo that can support it close to steady state. We fit equilibrium orbit-superposition (Schwarzschild) models to the stellar halo in a realistic Milky Way potential with a tilted dark m…

Didn’t know these quotes, but I love writing code ina world of constraints.
(From “math with bad drawings”)

Exploring the conditions for sustainability with open-ended innovation
Debora Princepe, Cristobal Qui\~ninao, Cristina D\'iaz Faloh, Pablo A. Marquet, Matteo Marsili
https://arxiv.org/abs/2510.01085

Exploring the conditions for sustainability with open-ended innovation
Can sustained open-ended technological progress preserve natural resources in a finite planet? We address this question on the basis of a stylized model with genuine open-ended technological innovation, where an innovation event corresponds to a random draw of a technology in the space of the parameters that define how it impacts the environment and how it interacts with the population. Technological innovation is endogenous because an innovation may invade if it satisfies constraints which dep…

Limiting the Parameter Space for Unstable eV-scale Neutrinos Using IceCube Data
Abbasi, Ackermann, Adams, Agarwalla, Aguilar, Ahlers, Alameddine, Ali, Amin, Andeen, Arg\"uelles, Ashida, Athanasiadou, Axani, Babu, Bai, Baines-Holmes, V., Barwick, Bash, Basu, Bay, Beatty, Tjus, Behrens, Beise, Bellenghi, Benkel, BenZvi, Berley, Bernardini, Besson, Blaufuss, Bloom, Blot, Bodo, Bontempo, Motzkin, Meneguolo, B\"oser, Botner, B\"ottcher, Braun, Brinson, Brisson-Tsavoussis, Bur…

Limiting the Parameter Space for Unstable eV-scale Neutrinos Using IceCube Data
This Letter extends a recent IceCube sterile neutrino search to include unstable sterile neutrinos within the context of a model termed 3+1+Decay, which expands upon the 3+1 model by introducing sterile neutrino decay to invisible particles with coupling constant $g^2$. The model is attractive since it reduces tension between oscillation experiments within the global fits and with constraints that come from cosmological observables. The analysis uses 10.7 years of up-going muon neutrino data wi…

The Theory of Topo-Symmetric Extensions of Topological Groups
Es-said En-naoui
https://arxiv.org/abs/2510.00018 https://arxiv.org/pdf/2510.00018

The Theory of Topo-Symmetric Extensions of Topological Groups
We introduce the notion of \emph{topo-symmetric extensions} of topological groups, a new generalization of classical group extensions that incorporates both topological and symmetry constraints. We define morphisms between such extensions, construct the associated groupoid, and develop classification results in terms of adapted cohomology. Several new invariants are introduced, including dimension, stabilizer, and density invariants, which characterize the fine structure of these extensions. Ap…

Optimal placement of wind farms via quantile constraint learning
Wenxiu Feng, Antonio Alc\'antara, Carlos Ruiz
https://arxiv.org/abs/2510.01093 https://

Optimal placement of wind farms via quantile constraint learning
Wind farm placement arranges the size and the location of multiple wind farms within a given region. The power output is highly related to the wind speed on spatial and temporal levels, which can be modeled by advanced data-driven approaches. To this end, we use a probabilistic neural network as a surrogate that accounts for the spatiotemporal correlations of wind speed. This neural network uses ReLU activation functions so that it can be reformulated as mixed-integer linear set of constraints …

Annealed Ensemble Kalman Inversion for Constrained Nonlinear Model Predictive Control: An ADMM Approach
Ahmed Khalil, Mohamed Safwat, Efstathios Bakolas
https://arxiv.org/abs/2510.00559

Annealed Ensemble Kalman Inversion for Constrained Nonlinear Model Predictive Control: An ADMM Approach
This work proposes a novel Alternating Direction Method of Multipliers (ADMM)-based Ensemble Kalman Inversion (EKI) algorithm for solving constrained nonlinear model predictive control (NMPC) problems. First, the stage-wise nonlinear inequality constraints in the NMPC problem are embedded via an augmented Lagrangian with nonnegative slack variables. We then show that the unconstrained augmented Lagrangian formulation of the NMPC admits a Bayesian interpretation: under a Gaussian observation mod…

How constraints fuel creativity!
https://www.youtube.com/watch?v=HoUMdM3x5Dw

Manchester’s Local Plan – responding to the consultation
Local Plans are what guide the pattern of a land use and and building across a council area.  In the case of Greater Manchester (excluding Stockport), the Joint Strategic Plan, Places for Everyone sets the scene, as does the government's National Planning Policy Framework.  That means that there are some very real constraints on what can be put into a so-called Local Plan (not very local - in the case of…

Manchester’s Local Plan – responding to the consultation
UPDATE!  11 Nov.  We have now submitted a revised version.  It adds some answers to questions we didn’t address in the previously published draft, and has some additions to some of the other …

ChatGPT Atlas hands-on: generally able to interpret instructions and navigate simple menus, but "technical constraints on session length" are a limiting factor (Kyle Orland/Ars Technica)
https://arstechnica.com/features/2025/

We let OpenAI’s “Agent Mode” surf the web for us—here’s what happened
From scanning emails to building fansites, Atlas can ably automate some web-based tasks.

The Atacama #Cosmology Telescope: DR6 maps / power spectra, likelihoods and ΛCDM parameters / constraints on extended cosmological models: https://iopscience.iop.org/article/10.1088/1475-7516/2025/11/061 / https://iopscience.iop.org/article/10.1088/1475-7516/2025/11/062 / https://iopscience.iop.org/article/10.1088/1475-7516/2025/11/063 -> ACT releases its final data, shaping the future of cosmology: https://www.eurekalert.org/news-releases/1106637

Ukrainian Forces in Mirnohrad face operational encirclement amid severe logistics constraints, MP says: https://benborges.xyz/2025/12/23/ukrainian-forces-in-mirnohrad-face.html

Multi-Armed Bandits with Minimum Aggregated Revenue Constraints
Ahmed Ben Yahmed, Hafedh El Ferchichi, Marc Abeille, Vianney Perchet
https://arxiv.org/abs/2510.12523 https://

Multi-Armed Bandits with Minimum Aggregated Revenue Constraints
We examine a multi-armed bandit problem with contextual information, where the objective is to ensure that each arm receives a minimum aggregated reward across contexts while simultaneously maximizing the total cumulative reward. This framework captures a broad class of real-world applications where fair revenue allocation is critical and contextual variation is inherent. The cross-context aggregation of minimum reward constraints, while enabling better performance and easier feasibility, intro…

At what point does anyone think it will be economically feasible for a government to speculatively decode bulk intercepts of circa 2025 traffic (using, say, 2048 bit RSA keys) using quantum computers?
As in you've got 100k qubits you're not otherwise using, and no real energy constraints.
Because I've seen that used as reason not to allow Royal Mint Court to be used by China.

I think we can actually prove that this constraint is the *only* constraint that can preserve freedom:
1. There will exist actors in a system who will wish to take advantage of others. Evolution drives survival and one strategy for increasing survival in an altruistic society is to become a parasite.
2. Expecting exploitative dynamics, a system needs to have a set of rules to manage exploitation.
3. If the set of rules is static it will lack the requisite variety necessary to manage the infinite possible behavior of humans so the system will fail.
4. If the system is dynamic then it must have a rule set about how it's own rules are updated. This would make the system recursive, which makes the system at least as complex as mathematics. Any system at least as complex as mathematics is necessarily either incomplete or inconsistent (Gödel's incompleteness theorem). If the system is incomplete, then constraints can be evaded which then allow a malicious agent to seize control of the system and update the rules for their own benefit. If constraints are incomplete, then a malicious agent can take advantage of others within the system.
5. Therefore, no social system can possibly protect freedom unless there exists a single metasystemic constraint (that the system must be optional) allowing for the system to be abandoned when compromised.
Oh, you might say, but this just means you have to infinitely abandon systems. Sure, but there's an evolutionary advantage to cooperation so there's evolutionary pressure to *not* be a malicious actor. So a malicious actor being able to compromise the whole system is likely to be a much more rare event. Compromising a system is a lot of work, so the first thing a malicious actor would want to do is preserve that work. They would want to lock you in. The most important objective to a malicious actor compromising a system would be to violate that metasystemic constraint, or all of their work goes out the window when everyone leaves.
And now you understand why borders exist, why fascists are obsessed with maintaining categories like gender, race, ethnicity, etc. This is why even Democrats like Newsom are on board with putting houseless people in concentration camps. And this is why the most important thing anarchists promote is the ability to choose not to be part of any of that.

Orientation Learning and Adaptation towards Simultaneous Incorporation of Multiple Local Constraints
Gaofeng Li, Peisen Xu, Ruize Wang, Qi Ye, Jiming Chen, Dezhen Song, Yanlong Huang
https://arxiv.org/abs/2510.07986

Orientation Learning and Adaptation towards Simultaneous Incorporation of Multiple Local Constraints
Orientation learning plays a pivotal role in many tasks. However, the rotation group SO(3) is a Riemannian manifold. As a result, the distortion caused by non-Euclidean geometric nature introduces difficulties to the incorporation of local constraints, especially for the simultaneous incorporation of multiple local constraints. To address this issue, we propose the Angle-Axis Space-based orientation representation method to solve several orientation learning problems, including orientation adap…

Encoding Numeric Computations and Infusing Heuristic Knowledge Using Integrity Constraints in stableKanren
Xiangyu Guo, Ajay Bansal
https://arxiv.org/abs/2510.04049 https://

Encoding Numeric Computations and Infusing Heuristic Knowledge Using Integrity Constraints in stableKanren
This paper presents examples of using integrity constraints in stableKanren to encode numeric computations for problem solving. Then, we use one of the examples to introduce multiple ways to infuse heuristic knowledge and reduce solving time. stableKanren is an extension of miniKanren that supports normal logic programs under stable model semantics. stableKanren further supports numeric computation by constructing a constraint store for integrity constraints. There are three ways to extend a re…

Robust Closed-Form Control for MIMO Nonlinear Systems under Conflicting Time-Varying Hard and Soft Constraints
Farhad Mehdifar, Charalampos P. Bechlioulis, Dimos V. Dimarogonas
https://arxiv.org/abs/2510.11393

Robust Closed-Form Control for MIMO Nonlinear Systems under Conflicting Time-Varying Hard and Soft Constraints
This paper introduces a novel robust closed-form control law to handle time-varying hard and soft constraints in uncertain high-relative-degree nonlinear MIMO systems. These constraints represent spatiotemporal specifications in mechanical systems' operational space, with hard constraints ensuring safety-critical requirements and soft constraints encoding performance or task objectives. Initially, all constraints are consolidated into two separate scalar time-varying hard and soft constraint fu…

Oof, fuck microsoft.
But also, there's no reason to use permissive licenses any more. "Open source" has taken over the world. We no longer have to work with billion-dollar companies in order to get our stuff used. Now we need to protect ourselves from being taken advantage of by them.
https://

Existence of ghost-eliminating constraints in multivielbein theory
J. Flinckman, S. F. Hassan
https://arxiv.org/abs/2510.03014 https://arxiv.org/pdf/2510.0…

Existence of ghost-eliminating constraints in multivielbein theory
We perform a Hamiltonian constraint analysis of the multivielbein theory proposed in arXiv:1804.09723. The analysis shows that the secondary constraints have the correct form to constrain the dynamical variables, thereby eliminating the problematic ghost modes that generally plague theories of interacting spin-2 fields. Under mild restrictions on the solution space, we also identify the tertiary constraints and show that these eliminate the canonical momenta associated with the ghost modes. Our…

Volume-Based Lower Bounds to the Capacity of the Gaussian Channel Under Pointwise Additive Input Constraints
Neri Merhav (Shitz), Shlomo Shamai (Shitz)
https://arxiv.org/abs/2510.04095

Volume-Based Lower Bounds to the Capacity of the Gaussian Channel Under Pointwise Additive Input Constraints
We present a family of relatively simple and unified lower bounds on the capacity of the Gaussian channel under a set of pointwise additive input constraints. Specifically, the admissible channel input vectors $\bx = (x_1, \ldots, x_n)$ must satisfy $k$ additive cost constraints of the form $\sum_{i=1}^n ϕ_j(x_i) \le n Γ_j$, $j = 1,2,\ldots,k$, which are enforced pointwise for every $\bx$, rather than merely in expectation. More generally, we also consider cost functions that depend on a slid…

Sometimes I understand why people design new standards or formats when the existing ones can’t be extended (whether due to practical constraints, authority, or bureaucracy) or simply aren’t flexible enough to evolve. I think the XKCD comic doesn’t include that aspect.
And yes, maybe new standards will appear.
#nocontext

“Israeli soldiers have described a free-for-all in Gaza and a breakdown in norms and legal constraints, with civilians killed at the whim of individual officers, according to testimony in a TV documentary.
“If you want to shoot without restraint, you can,” Daniel, the commander of an Israel Defense Forces (IDF) tank unit, says in Breaking Ranks: Inside Israel’s War, due to be broadcast in the UK on ITV on Monday evening.
Some of the IDF soldiers who talked to the programme reques…

Industry executives say nearly all major memory chipmakers are running at or near full capacity, with 2026 production slots almost "sold out" due to AI demand (Nikkei Asia)
https://asia.nikkei.com/business/technolog…

Rampant AI demand throws the memory chip market into turmoil
Supply constraints spark warnings of price rises and launch delays for consumer electronics

NaViL: Rethinking Scaling Properties of Native Multimodal Large Language Models under Data Constraints
Changyao Tian, Hao Li, Gen Luo, Xizhou Zhu, Weijie Su, Hanming Deng, Jinguo Zhu, Jie Shao, Ziran Zhu, Yunpeng Liu, Lewei Lu, Wenhai Wang, Hongsheng Li, Jifeng Dai
https://arxiv.org/abs/2510.08565 …

NaViL: Rethinking Scaling Properties of Native Multimodal Large Language Models under Data Constraints
Compositional training has been the de-facto paradigm in existing Multimodal Large Language Models (MLLMs), where pre-trained vision encoders are connected with pre-trained LLMs through continuous multimodal pre-training. However, the multimodal scaling property of this paradigm remains difficult to explore due to the separated training. In this paper, we focus on the native training of MLLMs in an end-to-end manner and systematically study its design space and scaling property under a practica…

"In summary, #AGI, as commonly conceived, will not happen because it ignores the physical constraints of computation, the exponential costs of linear progress, and the fundamental limits we are already encountering. Superintelligence is a fantasy because it assumes that intelligence can recursively self-improve without bound, ignoring the physical and economic realities that constrain all systems. …

Remoteness, order, size and connectivity constraints in digraphs
Sufiyan Mallu
https://arxiv.org/abs/2510.08841 https://arxiv.org/pdf/2510.08841

Remoteness, order, size and connectivity constraints in digraphs
Let \( D \) be a strongly connected digraph. The average distance of a vertex \( v \) in \( D \) is defined as the arithmetic mean of the distances from \( v \) to all other vertices in \( D \). The remoteness \( ρ(D) \) of \( D \) is the maximum of the average distances of the vertices in \( D \). In this paper, we provide a sharp upper bound on the remoteness of a strong digraph with given order, size, and vertex-connectivity. We then characterise the extremal digraphs that maximise remote…

Constraint-Guided Unit Test Generation for Machine Learning Libraries
Lukas Krodinger, Altin Hajdari, Stephan Lukasczyk, Gordon Fraser
https://arxiv.org/abs/2510.09108 https://

Constraint-Guided Unit Test Generation for Machine Learning Libraries
Machine learning (ML) libraries such as PyTorch and TensorFlow are essential for a wide range of modern applications. Ensuring the correctness of ML libraries through testing is crucial. However, ML APIs often impose strict input constraints involving complex data structures such as tensors. Automated test generation tools such as Pynguin are not aware of these constraints and often create non-compliant inputs. This leads to early test failures and limited code coverage. Prior work has investig…

Fair Rent Division: New Budget and Rent Constraints
Rohith Reddy Gangam, Shayan Taherijam, Vijay V. Vazirani
https://arxiv.org/abs/2510.05434 https://arxiv…

Fair Rent Division: New Budget and Rent Constraints
We study the classical rent division problem, where $n$ agents must allocate $n$ indivisible rooms and split a fixed total rent $R$. The goal is to compute an envy-free (EF) allocation, where no agent prefers another agent's room and rent to their own. This problem has been extensively studied under standard assumptions, where efficient algorithms for computing EF allocations are known. We extend this framework by introducing two practically motivated constraints: (i) lower and upper bounds o…

First simultaneous global QCD analysis of kaon and pion parton distributions with lattice QCD constraints
P. C. Barry, Chueng-Ryong Ji, W. Melnitchouk, N. Sato, Fernanda Steffens
https://arxiv.org/abs/2510.11979

First simultaneous global QCD analysis of kaon and pion parton distributions with lattice QCD constraints
We perform the first simultaneous global QCD analysis of pion and kaon parton distribution functions (PDFs), constrained by pion- and kaon-induced Drell-Yan (DY) and leading neutron electroproduction data, together with lattice QCD data on pion and kaon PDF moments. The analysis indicates a softer valence $\bar u$ distribution in the $K^-$ than in the $π^-$, and a significantly more peaked valence $s$-quark density in $K^-$ compared with the $\bar u$. The effective exponent governing the high-…

from my link log —
Should CSS be a constraint system instead?
https://pavpanchekha.com/blog/why-css-bad.html
saved 2025-12-06 https://

Because #accessibility is out of scope for Baseline, devs may not know how poorly supported a feature is and miss an opportunity to vote for things that are barriers to users and risks to orgs.
“Vote for the web features you want to see”
https://

Bridging the Physics-Data Gap with FNO-Guided Conditional Flow Matching: Designing Inductive Bias through Hierarchical Physical Constraints
Tsuyoshi Okita
https://arxiv.org/abs/2510.08295

Bridging the Physics-Data Gap with FNO-Guided Conditional Flow Matching: Designing Inductive Bias through Hierarchical Physical Constraints
Conventional time-series generation often ignores domain-specific physical constraints, limiting statistical and physical consistency. We propose a hierarchical framework that embeds the inherent hierarchy of physical laws-conservation, dynamics, boundary, and empirical relations-directly into deep generative models, introducing a new paradigm of physics-informed inductive bias. Our method combines Fourier Neural Operators (FNOs) for learning physical operators with Conditional Flow Matching (C…

Moody Urbanity - Manmade Confinements ⛑️
情绪化城市 - 人造禁锢 ⛑️
📷 Pentax MX
🎞️ Kentmere Pan 400
#filmphotography #Photography #blackandwhite

Learning to Look at the Other Side: A Semantic Probing Study of Word Embeddings in LLMs with Enabled Bidirectional Attention
Zhaoxin Feng, Jianfei Ma, Emmanuele Chersoni, Xiaojing Zhao, Xiaoyi Bao
https://arxiv.org/abs/2510.01652

Learning to Look at the Other Side: A Semantic Probing Study of Word Embeddings in LLMs with Enabled Bidirectional Attention
Autoregressive Large Language Models (LLMs) demonstrate exceptional performance in language understanding and generation. However, their application in text embedding tasks has been relatively slow, along with the analysis of their semantic representation in probing tasks, due to the constraints of the unidirectional attention mechanism. This paper aims to explore whether such constraints can be overcome by enabling bidirectional attention in LLMs. We tested different variants of the Llama ar…

🌊 Offshore Pumped Hydro Could Solve Clean Energy's Biggest Problem
https://oilprice.com/Energy/Energy-General/Offshore-Pumped-Hydro-Could-Solve-Clean-Energys-Biggest-Problem.html

Offshore Pumped Hydro Could Solve Clean Energy's Biggest Problem
An Italian startup is developing an offshore pumped hydro storage system that avoids the environmental and geographical constraints of traditional land-based dams, offering a potentially disruptive solution for long-duration energy storage and grid stability.

HardcoreLogic: Challenging Large Reasoning Models with Long-tail Logic Puzzle Games
Jingcong Liang, Shijun Wan, Xuehai Wu, Siyuan Wang, Yitong Li, Qianglong Chen, Duyu Tang, Zhongyu Wei
https://arxiv.org/abs/2510.12563

HardcoreLogic: Challenging Large Reasoning Models with Long-tail Logic Puzzle Games
Large Reasoning Models (LRMs) have demonstrated impressive performance on complex tasks, including logical puzzle games that require deriving solutions satisfying all constraints. However, whether they can flexibly apply appropriate rules to varying conditions, particularly when faced with non-canonical game variants, remains an open question. Existing corpora focus on popular puzzles like 9x9 Sudoku, risking overfitting to canonical formats and memorization of solution patterns, which can mask…

I had missed that last month, the condo development in the south parking lot of Fairview Mall was scaled back (at least for the first phase)
It's now just 1 tower instead of 3, and it's a rental instead of condos.
It's a really bad market to sell condos right now, so it's not too surprising.

Today, I designed, built and launched a small software application within very strict financial, security, hardware and software constraints.
Then I helped people with all levels of tech skill, and different hardware and software configurations, get it running with their own setups.
That's a lot of corporate speak and being vague, but it feels good to have it out in the world helping people: letting them get a very specific thing done better.

EB-MBD: Emerging-Barrier Model-Based Diffusion for Safe Trajectory Optimization in Highly Constrained Environments
Raghav Mishra, Ian R. Manchester
https://arxiv.org/abs/2510.07700

EB-MBD: Emerging-Barrier Model-Based Diffusion for Safe Trajectory Optimization in Highly Constrained Environments
We propose enforcing constraints on Model-Based Diffusion by introducing emerging barrier functions inspired by interior point methods. We show that constraints on Model-Based Diffusion can lead to catastrophic performance degradation, even on simple 2D systems due to sample inefficiency in the Monte Carlo approximation of the score function. We introduce Emerging-Barrier Model-Based Diffusion (EB-MBD) which uses progressively introduced barrier constraints to avoid these problems, significantl…

Low-redshift constraints on structure growth from CMB lensing tomography
Andrea Rubiola, Matteo Zennaro, Carlos Garc\'ia-Garc\'ia, David Alonso
https://arxiv.org/abs/2510.09563

Low-redshift constraints on structure growth from CMB lensing tomography
We present constraints on the amplitude of matter fluctuations from the clustering of galaxies and their cross-correlation with the gravitational lensing convergence of the cosmic microwave background (CMB), focusing on low redshifts ($z\lesssim0.3$), where potential deviations from a perfect cosmological constant dominating the growth of structure could be more prominent. Specifically, we make use of data from the 2MASS photometric survey (\tmpz) and the \wisc galaxy survey, in combination wit…

Dynamic Regret Bounds for Online Omniprediction with Long Term Constraints
Yahav Bechavod, Jiuyao Lu, Aaron Roth
https://arxiv.org/abs/2510.07266 https://a…

Dynamic Regret Bounds for Online Omniprediction with Long Term Constraints
We present an algorithm guaranteeing dynamic regret bounds for online omniprediction with long term constraints. The goal in this recently introduced problem is for a learner to generate a sequence of predictions which are broadcast to a collection of downstream decision makers. Each decision maker has their own utility function, as well as a vector of constraint functions, each mapping their actions and an adversarially selected state to reward or constraint violation terms. The downstream dec…

Set to Be Fair: Demographic Parity Constraints for Set-Valued Classification
Eyal Cohen (LPSM), Christophe Denis (SAMM), Mohamed Hebiri (LAMA)
https://arxiv.org/abs/2510.04926 h…

Set to Be Fair: Demographic Parity Constraints for Set-Valued Classification
Set-valued classification is used in multiclass settings where confusion between classes can occur and lead to misleading predictions. However, its application may amplify discriminatory bias motivating the development of set-valued approaches under fairness constraints. In this paper, we address the problem of set-valued classification under demographic parity and expected size constraints. We propose two complementary strategies: an oracle-based method that minimizes classification risk while…

Observational Constraints on Chaplygin Gas Models in Non-Minimally Coupled Power Law $f(Q)$ Gravity with Quasars
Nakul Aggarwal, Ali Pourmand, Fatimah Shojai, Harish Parthasarathy
https://arxiv.org/abs/2510.12472

Observational Constraints on Chaplygin Gas Models in Non-Minimally Coupled Power Law $f(Q)$ Gravity with Quasars
In the framework of $f(Q)$ gravity, where gravity emerges from non-metricity $Q$, we explore the cosmological implications of its non-minimal coupling to matter. Inspired by the recent success of Chaplygin gas models in explaining dark energy, we consider a background fluid composed of baryonic matter, radiation, and a family of Chaplygin gas variants namely Generalized Chaplygin Gas (GCG), Modified Chaplygin Gas (MCG), and Variable Chaplygin Gas (VCG). We constrain these models with three rece…

Inverse Mixed-Integer Programming: Learning Constraints then Objective Functions
Akira Kitaoka
https://arxiv.org/abs/2510.04455 https://arxiv.org/pdf/2510.…

Inverse Mixed-Integer Programming: Learning Constraints then Objective Functions
In mixed-integer linear programming, data-driven inverse optimization that learns the objective function and the constraints from observed data plays an important role in constructing appropriate mathematical models for various fields, including power systems and scheduling. However, to the best of our knowledge, there is no known method for learning both the objective functions and the constraints. In this paper, we propose a two-stage method for a class of problems where the objective functio…

Constraints on dark matter models from the stellar cores observed in ultra-faint dwarf galaxies: Self-interacting dark matter
Jorge Sanchez Almeida (Instituto de Astrofisica de Canarias, La Laguna, Tenerife, E-38200, Spain, Departamento de Astrofisica, Universidad de La Laguna, Spain)
https://arxiv.org/abs/2510.05682

Constraints on dark matter models from the stellar cores observed in ultra-faint dwarf galaxies: Self-interacting dark matter
It has been proposed that the stellar cores observed in ultra-faint dwarf (UFD) galaxies reflect underlying dark matter (DM) cores that cannot be formed by stellar feedback acting on collisionless cold dark matter (CDM) halos. Assuming this claim is correct, we investigate the constraints that arise if such cores are produced by self-interacting dark matter (SIDM). We derive the range of SIDM cross-sections (sigma/m) required to reproduce the observed core sizes. These can result from halos in …

from my link log —
Let's take esoteric programming languages seriously.
https://arxiv.org/abs/2505.15327
saved 2025-10-11 https://dotat.at/:/XKTKR.…

Let's Take Esoteric Programming Languages Seriously
Esoteric programming languages are challenging to learn, but their unusual features and constraints may serve to improve programming ability. From languages designed to be intentionally obtuse (e.g. INTERCAL) to others targeting artistic expression (e.g. Piet) or exploring the nature of computation (e.g. Fractan), there is rich variety in the realm of esoteric programming languages. This essay examines the counterintuitive appeal of esoteric languages and seeks to analyse reasons for this popul…

Physics-Informed Reinforcement Learning for Large-Scale EV Smart Charging Considering Distribution Network Voltage Constraints
Stavros Orfanoudakis, Frans Oliehoek, Peter Palesnky, Pedro P. Vergara
https://arxiv.org/abs/2510.12335

Physics-Informed Reinforcement Learning for Large-Scale EV Smart Charging Considering Distribution Network Voltage Constraints
Electric Vehicles (EVs) offer substantial flexibility for grid services, yet large-scale, uncoordinated charging can threaten voltage stability in distribution networks. Existing Reinforcement Learning (RL) approaches for smart charging often disregard physical grid constraints or have limited performance for complex large-scale tasks, limiting their scalability and real-world applicability. This paper introduces a physics-informed (PI) RL algorithm that integrates a differentiable power flow m…

A Systematic Literature Review of Machine Learning Techniques for Observational Constraints in Cosmology
Luis Rojas, Sebasti\'an Espinoza, Esteban Gonz\'alez, Carlos Maldonado, Fei Luo
https://arxiv.org/abs/2510.09876

A Systematic Literature Review of Machine Learning Techniques for Observational Constraints in Cosmology
This paper presents a systematic literature review focusing on the application of machine learning techniques for deriving observational constraints in cosmology. The goal is to evaluate and synthesize existing research to identify effective methodologies, highlight gaps, and propose future research directions. Our review identifies several key findings: (1) various machine learning techniques, including Bayesian neural networks, Gaussian processes, and deep learning models, have been applied t…

Localized structures in two-field systems: exact solutions in the presence of Lorentz symmetry breaking and explicit connection with geometric constraints
G. H. Bandeira, D. Bazeia, G. S. Santiago, Ya. Shnir
https://arxiv.org/abs/2510.07012

Localized structures in two-field systems: exact solutions in the presence of Lorentz symmetry breaking and explicit connection with geometric constraints
We investigate a class of models described by two real scalar fields in two-dimensional spacetime. The study focuses mainly on the presence of exact static solutions which satisfy the first-order formalism, in models constructed to engender Lorentz symmetry violation. We start by exploring a direct connection between Lorentz breaking and geometric constraint, as experimentally examined in the case of domain walls in geometrically constrained magnetic materials. By means of a specific choice of …

TDCOSMO 2025 - Cosmological constraints from #StrongLensing time delays: https://www.aanda.org/articles/aa/full_html/2025/12/aa55801-25/aa55801-25.html -> A speed camera for the universe: https://www.u-tokyo.ac.jp/focus/en/press/z0508_00434.html - researchers exploit gravitational lensing to see how fast the universe is really expanding.

Beyond Cons: Purely Relational Data Structures
Rafaello Sanna, William E. Byrd, Nada Amin
https://arxiv.org/abs/2510.03170 https://arxiv.org/pdf/2510.03170…

Beyond Cons: Purely Relational Data Structures
We present {Kanren} (read: set-Kanren), an extension to miniKanren with constraints for reasoning about sets and association lists. {Kanren} includes first-class set objects, a functionally complete family of set-theoretic constraints (including membership, union, and disjointedness), and new constraints for reasoning about association lists with shadowing and scoped lookup. These additions allow programmers to describe collections declaratively and lazily, without relying on structural encodin…

Constraints on inelastic dark matter from the CDEX-1B experiment
Y. F. Liang, L. T. Yang, Q. Yue, K. J. Kang, Y. J. Li, H. P. An, Greeshma C., J. P. Chang, H. Chen, Y. H. Chen, J. P. Cheng, J. Y. Cui, W. H. Dai, Z. Deng, Y. X. Dong, C. H. Fang, H. Gong, Q. J. Guo, T. Guo, X. Y. Guo, L. He, J. R. He, H. X. Huang, T. C. Huang, S. Karmakar, Y. S. Lan, H. B. Li, H. Y. Li, J. M. Li, J. Li, M. C. Li, Q. Y. Li, R. M. J. Li, X. Q. Li, Y. L. Li, B. Liao, F. K. Lin, S. T. Lin, J. X. Liu, R. Z. L…

Constraints on inelastic dark matter from the CDEX-1B experiment
We present limits on spin-independent inelastic WIMP-nucleus scattering using the 737.1 kg $\cdot$ day dataset from the CDEX-1B experiment. Expected nuclear recoil spectra for various inelastic WIMP masses $m_χ$ and mass splittings $δ$ are calculated under the standard halo model. An accurate background model of CDEX-1B is constructed by simulating all major background sources. The model parameters are then determined through maximum likelihood estimation and Markov Chain Monte Carlo fitting.…

Big Bang Nucleosynthesis constraints on space-time noncommutativity
Teodora Maria Matei, Cristian Croitoru, Tiberiu Harko
https://arxiv.org/abs/2510.10685 https://

Big Bang Nucleosynthesis constraints on space-time noncommutativity
We consider the implications of the modified dispersion relations, due to the noncommutativity of the spacetime, for a photon gas filling the early Universe in the framework of the Big Bang Nucleosynthesis (BBN) processes, during the period of light elements formation. We consider three types of deformations present in the dispersion relations for the radiation gas, from which we obtain the low temperature corrections to the energy density and pressure. The cosmological implications of the modi…

Efficient Perceptual Image Super Resolution: AIM 2025 Study and Benchmark
Bruno Longarela, Marcos V. Conde, Alvaro Garcia, Radu Timofte
https://arxiv.org/abs/2510.12765 https://…

Efficient Perceptual Image Super Resolution: AIM 2025 Study and Benchmark
This paper presents a comprehensive study and benchmark on Efficient Perceptual Super-Resolution (EPSR). While significant progress has been made in efficient PSNR-oriented super resolution, approaches focusing on perceptual quality metrics remain relatively inefficient. Motivated by this gap, we aim to replicate or improve the perceptual results of Real-ESRGAN while meeting strict efficiency constraints: a maximum of 5M parameters and 2000 GFLOPs, calculated for an input size of 960x540 pixels…

Probing Dark Matter Interactions with Stellar Motion near Sagittarius A*
R. Andrew Gustafson, Ian M. Shoemaker, Volodymyr Takhistov
https://arxiv.org/abs/2510.07387 https://

Probing Dark Matter Interactions with Stellar Motion near Sagittarius A*
Stars orbiting Sgr A* at the Milky Way's center provide a unique laboratory to test gravity and dark matter (DM). We demonstrate that DM interactions in stellar interiors induce a novel momentum transfer force, altering orbits beyond gravitational effects. Using S2's 2000-2019 orbital data we derive the first astrophysical constraints on DM-nucleon scattering, excluding new sub-GeV parameter space. Stellar lifetime constraints over Myr timescales complement these, surpassing some direct detecti…

Sequential Convex Programming for 6-DoF Powered Descent Guidance with Continuous-Time Compound State-Triggered Constraints
Samet Uzun, Behcet Acikmese, John M. Carson III
https://arxiv.org/abs/2510.09610

Sequential Convex Programming for 6-DoF Powered Descent Guidance with Continuous-Time Compound State-Triggered Constraints
This paper presents a sequential convex programming (SCP) framework for ensuring the continuous-time satisfaction of compound state-triggered constraints, a subset of logical specifications, in the powered descent guidance (PDG) problem. The proposed framework combines the generalized mean-based smooth robustness measure (D-GMSR), a parameterization technique tailored for expressing discrete-time temporal and logical specifications through smooth functions, with the continuous-time successive c…

Do Large Language Models Respect Contracts? Evaluating and Enforcing Contract-Adherence in Code Generation
Soohan Lim, Joonghyuk Hahn, Hyunwoo Park, Sang-Ki Ko, Yo-Sub Han
https://arxiv.org/abs/2510.12047

Do Large Language Models Respect Contracts? Evaluating and Enforcing Contract-Adherence in Code Generation
Prevailing code generation benchmarks, such as HumanEval+ and MBPP+, primarily evaluate large language models (LLMs) with pass@k on functional correctness using well-formed inputs. However, they ignore a crucial aspect of real-world software: adherence to contracts-the preconditions and validity constraints that dictate how ill-formed inputs must be rejected. This critical oversight means that existing benchmarks fail to measure, and models consequently fail to generate, truly robust and reliab…

Collaborative Edge Inference via Semantic Grouping under Wireless Channel Constraints
Mateus P. Mota, Mattia Merluzzi, Emilio Calvanese Strinati
https://arxiv.org/abs/2510.02222

Collaborative Edge Inference via Semantic Grouping under Wireless Channel Constraints
In this paper, we study the framework of collaborative inference, or edge ensembles. This framework enables multiple edge devices to improve classification accuracy by exchanging intermediate features rather than raw observations. However, efficient communication strategies are essential to balance accuracy and bandwidth limitations. Building upon a key-query mechanism for selective information exchange, this work extends collaborative inference by studying the impact of channel noise in featur…

Probing Latent Knowledge Conflict for Faithful Retrieval-Augmented Generation
Linfeng Gao, Baolong Bi, Zheng Yuan, Le Wang, Zerui Chen, Zhimin Wei, Shenghua Liu, Qinggang Zhang, Jinsong Su
https://arxiv.org/abs/2510.12460

Probing Latent Knowledge Conflict for Faithful Retrieval-Augmented Generation
Retrieval-Augmented Generation (RAG) has emerged as a powerful paradigm to enhance the factuality of Large Language Models (LLMs). However, existing RAG systems often suffer from an unfaithfulness issue, where the model's response contradicts evidence from the retrieved context. Existing approaches to improving contextual faithfulness largely rely on external interventions, such as prompt engineering, decoding constraints, or reward-based fine-tuning. These works treat the LLM as a black box an…

Smooth Quasar-Convex Optimization with Constraints
David Mart\'inez-Rubio
https://arxiv.org/abs/2510.01943 https://arxiv.org/pdf/2510.01943

Smooth Quasar-Convex Optimization with Constraints
Quasar-convex functions form a broad nonconvex class with applications to linear dynamical systems, generalized linear models, and Riemannian optimization, among others. Current nearly optimal algorithms work only in affine spaces due to the loss of one degree of freedom when working with general convex constraints. Obtaining an accelerated algorithm that makes nearly optimal $\widetilde{O}(1/(γ\sqrtε))$ first-order queries to a $γ$-quasar convex smooth function \emph{with constraints} was i…

What You Don't Know Can Hurt You: How Well do Latent Safety Filters Understand Partially Observable Safety Constraints?
Matthew Kim, Kensuke Nakamura, Andrea Bajcsy
https://arxiv.org/abs/2510.06492

What You Don't Know Can Hurt You: How Well do Latent Safety Filters Understand Partially Observable Safety Constraints?
Safe control techniques, such as Hamilton-Jacobi reachability, provide principled methods for synthesizing safety-preserving robot policies but typically assume hand-designed state spaces and full observability. Recent work has relaxed these assumptions via latent-space safe control, where state representations and dynamics are learned jointly through world models that reconstruct future high-dimensional observations (e.g., RGB images) from current observations and actions. This enables safety …

Entropy Regularizing Activation: Boosting Continuous Control, Large Language Models, and Image Classification with Activation as Entropy Constraints
Zilin Kang, Chonghua Liao, Tingqiang Xu, Huazhe Xu
https://arxiv.org/abs/2510.08549

Entropy Regularizing Activation: Boosting Continuous Control, Large Language Models, and Image Classification with Activation as Entropy Constraints
We propose ERA, a new paradigm that constrains the sampling entropy above given thresholds by applying specially designed activations to the outputs of models. Our approach demonstrates broad effectiveness across different domains: 1) for large language models(LLMs), boosting the AIME 2025 score for Qwen2.5-Math-7B by 37.4%; 2) for continuous control reinforcement learning agents, improving performance by more than 30% over strong baselines such as SAC on the challenging HumanoidBench; 3) for i…

Constraints on the Feasibility of #Ferrovolcanism on Asteroid 16 Psyche: https://agupubs.onlinelibrary.wiley.com/doi/10.1029/2024JE008811 -> Are There Metal Volcanoes on Asteroids? https://eos.org/editor-highlights/are-there-metal-volcanoes-on-asteroids

Updated constraints on interacting dark energy: A comprehensive analysis using multiple CMB probes, DESI DR2, and supernovae observations
Tian-Nuo Li, Guo-Hong Du, Yun-He Li, Yichao Li, Jia-Le Ling, Jing-Fei Zhang, Xin Zhang
https://arxiv.org/abs/2510.11363

Updated constraints on interacting dark energy: A comprehensive analysis using multiple CMB probes, DESI DR2, and supernovae observations
Recent DESI baryon acoustic oscillation (BAO) measurements, combined with Planck cosmic microwave background (CMB) data and DESY5 type Ia supernova (SN) data, indicate a significant deviation from $Λ$CDM, which seems to suggest that this deviation can be explained by an interaction between dark energy and dark matter. In this work, we perform a comprehensive analysis by utilizing the latest DESI DR2 BAO data in conjunction with CMB data from ACT, SPT, Planck, and WMAP, along with SN data from …

Mean-Field Games with Constraints
Anran Hu, Zijiu Lyu
https://arxiv.org/abs/2510.11843 https://arxiv.org/pdf/2510.11843

Mean-Field Games with Constraints
This paper introduces a framework of Constrained Mean-Field Games (CMFGs), where each agent solves a constrained Markov decision process (CMDP). This formulation captures scenarios in which agents' strategies are subject to feasibility, safety, or regulatory restrictions, thereby extending the scope of classical mean field game (MFG) models. We first establish the existence of CMFG equilibria under a strict feasibility assumption, and we further show uniqueness under a classical monotonicity co…

Efficient Real-World Deblurring using Single Images: AIM 2025 Challenge Report
Daniel Feijoo, Paula Garrido-Mellado, Marcos V. Conde, Jaesung Rim, Alvaro Garcia, Sunghyun Cho, Radu Timofte
https://arxiv.org/abs/2510.12788

Efficient Real-World Deblurring using Single Images: AIM 2025 Challenge Report
This paper reviews the AIM 2025 Efficient Real-World Deblurring using Single Images Challenge, which aims to advance in efficient real-blur restoration. The challenge is based on a new test set based on the well known RSBlur dataset. Pairs of blur and degraded images in this dataset are captured using a double-camera system. Participant were tasked with developing solutions to effectively deblur these type of images while fulfilling strict efficiency constraints: fewer than 5 million model para…

Constraints on Radial Gas Flows in the Milky Way Disk Revealed by Large Stellar Age Catalogs
James W. Johnson
https://arxiv.org/abs/2510.05223 https://arxi…

Constraints on Radial Gas Flows in the Milky Way Disk Revealed by Large Stellar Age Catalogs
Disk galaxies like the Milky Way are expected to experience gas flows carrying matter toward their centers. This paper investigates the role of these radial gas flows in models of Galactic chemical evolution (GCE). We follow five different parameterizations of the Galactocentric radial velocity, $v_{r,g}$, of the interstellar medium (ISM). Relative to the $v_{r,g}=0$ limit, all models predict stellar metallicity to decline less significantly with age in the outer disk and more significantly in …

Feasibility-Aware Decision-Focused Learning for Predicting Parameters in the Constraints
Jayanta Mandi, Marianne Defresne, Senne Berden, Tias Guns
https://arxiv.org/abs/2510.04951

Feasibility-Aware Decision-Focused Learning for Predicting Parameters in the Constraints
When some parameters of a constrained optimization problem (COP) are uncertain, this gives rise to a predict-then-optimize (PtO) problem, comprising two stages -- the prediction of the unknown parameters from contextual information and the subsequent optimization using those predicted parameters. Decision-focused learning (DFL) implements the first stage by training a machine learning (ML) model to optimize the quality of the decisions made using the predicted parameters. When parameters in the…

Automaton Constrained Q-Learning
Anastasios Manganaris, Vittorio Giammarino, Ahmed H. Qureshi
https://arxiv.org/abs/2510.05061 https://arxiv.org/pdf/2510.0…

Automaton Constrained Q-Learning
Real-world robotic tasks often require agents to achieve sequences of goals while respecting time-varying safety constraints. However, standard Reinforcement Learning (RL) paradigms are fundamentally limited in these settings. A natural approach to these problems is to combine RL with Linear-time Temporal Logic (LTL), a formal language for specifying complex, temporally extended tasks and safety constraints. Yet, existing RL methods for LTL objectives exhibit poor empirical performance in compl…

Pricing Short-Circuit Current via a Primal-Dual Formulation for Preserving Integrality Constraints
Peng Wang, Luis Badesa
https://arxiv.org/abs/2510.05293 https://

Pricing Short-Circuit Current via a Primal-Dual Formulation for Preserving Integrality Constraints
Synchronous Generators (SGs) currently provide important levels of Short-Circuit Current (SCC), a critical ancillary service that ensures line protections trip during short-circuit faults. Given the ongoing replacement of SGs by power-electronics-based generation, which have a hard limit for current injection, it has become relevant to optimize the procurement of SCC provided by remaining SGs. Pricing this service is however challenging due to the integrality constraints in Unit Commitment (UC)…

Observational constraints on f(Q,T) gravity from the mass-radius relation and stability of compact stars
S. K. Maurya, Abdul Aziz, Ksh. Newton Singh, G. Mustafa, Y. Sekhmani, Saibal Ray
https://arxiv.org/abs/2510.06003

Observational constraints on f(Q,T) gravity from the mass-radius relation and stability of compact stars
In this investigation we examine the astrophysical consequences of the influence of pressure anisotropy on the physical properties of observed pulsars within the background of $f(Q,T)$ gravity by choosing a specific form $f(Q, T)=ψ_1\, Q + ψ_2 T$, where $ψ_1$ and $ψ_2$ are the model parameters. Initially, we solve the modified field equations for anisotropic stellar configurations by assuming the physically valid metric potential along with anisotropic function for the distribution of the i…

JWST-TST DREAMS - NIRSpec/PRISM Transmission Spectroscopy of the Habitable Zone Planet TRAPPIST-1 e & Secondary Atmosphere Constraints for the Habitable Zone Planet TRAPPIST-1 e / The Photochemical Plausibility of Warm Exo-Titans Orbiting M Dwarf Stars: #TRAPPIST-1e, an Earth-sized, habitable-zone exoplanet: https://news.arizona.edu/news/new-look-trappist-1e-earth-sized-habitable-zone-exoplanet

Generalized Multi-Constraint Extremum Seeking
Alan Williams, Jorge Cort\'es, Alexander Scheinker
https://arxiv.org/abs/2510.06403 https://arxiv.org/pdf…

Generalized Multi-Constraint Extremum Seeking
We generalize the Safe Extremum Seeking algorithm to address the minimization of an unknown objective function subject to multiple unknown inequality and equality constraints, relying on recent results of gradient flow systems. These constraints may represent safety or other critical conditions. The proposed ES algorithm functions as a general nonlinear programming tool, offering practical maintenance of all constraints and semiglobal practical asymptotic stability, utilizing a Lyapunov argumen…

Constraints on the interacting holographic dark energy models: implications from background and perturbations data
N. Nazari Pooya
https://arxiv.org/abs/2510.08875 https://

Constraints on the interacting holographic dark energy models: implications from background and perturbations data
In this study, we employ a two-step method to analyze models of holographic dark energy (HDE) and interacting holographic dark energy (IHDE), incorporating three distinct dark energy (DE)-dark matter (DM) interaction terms. First, using the latest background dataset, we conduct a Markov chain Monte Carlo (MCMC) analysis to constrain the free parameters of the models. Then, we assess the models against each other using the key background parameters and compare them to the $Λ$CDM standard model.…

Empowering Prosumers: Incentive Design for Local Electricity Markets Under Generalized Uncertainty and Grid Constraints
P{\aa}l Forr Austnes, Matthieu Jacobs, Lu Wang, Mario Paolone
https://arxiv.org/abs/2510.12318

Empowering Prosumers: Incentive Design for Local Electricity Markets Under Generalized Uncertainty and Grid Constraints
Since the 1990s, widespread introduction of central (wholesale) electricity markets has been seen across multiple continents, driven by the search for efficient operation of the power grid through competition. The increase of renewables has made significant impacts both on central electricity markets and distribution-level grids as renewable power generation is often connected to the latter. These stochastic renewable technologies have both advantages and disadvantages. On one hand they offer v…

Optimal Regularization Under Uncertainty: Distributional Robustness and Convexity Constraints
Oscar Leong, Eliza O'Reilly, Yong Sheng Soh
https://arxiv.org/abs/2510.03464 ht…

Optimal Regularization Under Uncertainty: Distributional Robustness and Convexity Constraints
Regularization is a central tool for addressing ill-posedness in inverse problems and statistical estimation, with the choice of a suitable penalty often determining the reliability and interpretability of downstream solutions. While recent work has characterized optimal regularizers for well-specified data distributions, practical deployments are often complicated by distributional uncertainty and the need to enforce structural constraints such as convexity. In this paper, we introduce a frame…

How Well do Diffusion Policies Learn Kinematic Constraint Manifolds?
Lexi Foland, Thomas Cohn, Adam Wei, Nicholas Pfaff, Boyuan Chen, Russ Tedrake
https://arxiv.org/abs/2510.01404

How Well do Diffusion Policies Learn Kinematic Constraint Manifolds?
Diffusion policies have shown impressive results in robot imitation learning, even for tasks that require satisfaction of kinematic equality constraints. However, task performance alone is not a reliable indicator of the policy's ability to precisely learn constraints in the training data. To investigate, we analyze how well diffusion policies discover these manifolds with a case study on a bimanual pick-and-place task that encourages fulfillment of a kinematic constraint for success. We study …

Periodic Event-Triggered Prescribed Time Control of Euler-Lagrange Systems under State and Input Constraints
Chidre Shravista Kashyap, Karnan A, Pushpak Jagtap, Jishnu Keshavan
https://arxiv.org/abs/2510.02769

Periodic Event-Triggered Prescribed Time Control of Euler-Lagrange Systems under State and Input Constraints
This article proposes a periodic event-triggered adaptive barrier control policy for the trajectory tracking problem of perturbed Euler-Lagrangian systems with state, input, and temporal (SIT) constraints. In particular, an approximation-free adaptive-barrier control architecture is designed to ensure prescribed-time convergence of the tracking error to a prescribed bound while rejecting exogenous disturbances. In contrast to existing approaches that necessitate continuous real-time control act…

The value of storage in electricity distribution: The role of storage
Dirk Lauinger, Deepjyoti Deka, Sungho Shin
https://arxiv.org/abs/2510.12435 https://a…

The value of storage in electricity distribution: The role of storage
Electricity distribution companies deploy battery storage to defer grid upgrades by reducing peak demand. In deregulated jurisdictions, such storage often sits idle because regulatory constraints bar participation in electricity markets. Here, we develop an optimization framework that, to our knowledge, provides the first formal model of market participation constraints within storage investment and operation planning. Applying the framework to a Massachusetts case study, we find that market pa…

Multiprobe constraints on early and late time dark energy
Alexander Reeves, Simone Ferraro, Andrina Nicola, Alexandre Refregier
https://arxiv.org/abs/2510.06114 https://

Multiprobe constraints on early and late time dark energy
We perform a multiprobe analysis combining cosmic microwave background (CMB) data from Planck and the Atacama Cosmology Telescope (ACT), ACT CMB lensing, and large-scale structure (LSS) measurements from the Dark Energy Spectroscopic Instrument (DESI), including DESI Legacy Imaging Survey (LS) galaxies and baryon acoustic oscillations (BAOs). We present the first $5\times2$pt analysis of ACT DR6 lensing, DESI LS, and Planck ISW. Within $Λ$CDM, this yields $S_8 = σ_8(Ω_m/0.3)^{0.5} = 0.819 \p…

Heuristic Bundle Upper Bound Based Polyhedral Bundle Method for Semidefinite Programming
Zilong Cui, Ran Gu
https://arxiv.org/abs/2510.12374 https://arxiv.…

Heuristic Bundle Upper Bound Based Polyhedral Bundle Method for Semidefinite Programming
Semidefinite programming (SDP) is a fundamental class of convex optimization problems with diverse applications in mathematics, engineering, machine learning, and related disciplines. This paper investigates the application of the polyhedral bundle method to standard SDPs. The basic idea of this method is to approximate semidefinite constraints using linear constraints, and thereby transform the SDP problem into a series of quadratic programming subproblems. However, the number of linear constr…

Bandits with Single-Peaked Preferences and Limited Resources
Gur Keinan, Rotem Torkan, Omer Ben-Porat
https://arxiv.org/abs/2510.09425 https://arxiv.org/pd…

Bandits with Single-Peaked Preferences and Limited Resources
We study an online stochastic matching problem in which an algorithm sequentially matches $U$ users to $K$ arms, aiming to maximize cumulative reward over $T$ rounds under budget constraints. Without structural assumptions, computing the optimal matching is NP-hard, making online learning computationally infeasible. To overcome this barrier, we focus on \emph{single-peaked preferences} -- a well-established structure in social choice theory, where users' preferences are unimodal with respect to…

Semi-infinite Nonconvex Constrained Min-Max Optimization
Cody Melcher, Zeinab Alizadeh, Lindsey Hiett, Afrooz Jalilzadeh, Erfan Yazdandoost Hamedani
https://arxiv.org/abs/2510.12007

Semi-infinite Nonconvex Constrained Min-Max Optimization
Semi-Infinite Programming (SIP) has emerged as a powerful framework for modeling problems with infinite constraints, however, its theoretical development in the context of nonconvex and large-scale optimization remains limited. In this paper, we investigate a class of nonconvex min-max optimization problems with nonconvex infinite constraints, motivated by applications such as adversarial robustness and safety-constrained learning. We propose a novel inexact dynamic barrier primal-dual algorith…

Decentralized CBF-based Safety Filters for Collision Avoidance of Cooperative Missile Systems with Input Constraints
Johannes Autenrieb, Mark Spiller
https://arxiv.org/abs/2510.06846

Decentralized CBF-based Safety Filters for Collision Avoidance of Cooperative Missile Systems with Input Constraints
This paper presents a decentralized safety filter for collision avoidance in multi-agent aerospace interception scenarios. The approach leverages robust control barrier functions (RCBFs) to guarantee forward invariance of safety sets under bounded inputs and high-relative-degree dynamics. Each effector executes its nominal cooperative guidance command, while a local quadratic program (QP) modifies the input only when necessary. Event-triggered activation based on range and zero-effort miss (ZEM…

Adaptive Control Allocation for Underactuated Time-Scale Separated Non-Affine Systems
Daniel M. Cherenson, Dimitra Panagou
https://arxiv.org/abs/2510.07507 https://

Adaptive Control Allocation for Underactuated Time-Scale Separated Non-Affine Systems
Many robotic systems are underactuated, meaning not all degrees of freedom can be directly controlled due to lack of actuators, input constraints, or state-dependent actuation. This property, compounded by modeling uncertainties and disturbances, complicates the control design process for trajectory tracking. In this work, we propose an adaptive control architecture for uncertain, nonlinear, underactuated systems with input constraints. Leveraging time-scale separation, we construct a reduced-o…

Constraint Qualification for Generic Parameter Families of Constraints in Optimization
Naoki Hamada, Kenta Hayano, Hiroshi Teramoto
https://arxiv.org/abs/2510.02381 https://

Constraint Qualification for Generic Parameter Families of Constraints in Optimization
Constraint qualifications (CQs) are central to the local analysis of constrained optimization. In this paper, we completely determine the validity of the four classical CQs -- LICQ, MFCQ, ACQ, and GCQ -- for constraint map-germs that arise in generic four-parameter families. Our approach begins by proving that all four CQs are invariant under the action of the group $\mathcal{K}[G]$ and under the operation of reduction. As a consequence, the verification of CQ-validity for a generic constraint …

Efficient Input-Constrained Impulsive Optimal Control of Linear Systems with Application to Spacecraft Relative Motion
Ethan Foss, Simone D'Amico
https://arxiv.org/abs/2510.03423

Efficient Input-Constrained Impulsive Optimal Control of Linear Systems with Application to Spacecraft Relative Motion
This work presents a novel algorithm for impulsive optimal control of linear time-varying systems with the inclusion of input magnitude constraints. Impulsive optimal control problems, where the optimal input solution is a sum of delta functions, are typically formulated as an optimization over a normed function space subject to integral equality constraints and can be efficiently solved for linear time-varying systems in their dual formulation. In this dual setting, the problem takes the form …